Examples of ca.nengo.model.nef.NEFEnsembleFactory.make()

Class ca.nengo.model.nef.NEFEnsembleFactory

Examples of ca.nengo.model.nef.NEFEnsembleFactory.make()

ca.nengo.model.nef.NEFEnsembleFactory.make()
@param name Name of the NEFEnsemble @param n Number of neurons in the ensemble @param dim Dimension of the ensemble. @return NEFEnsemble containing Neurons generated with the default NeuronFactory @throws StructuralException if there is any error attempting to create the ensemble

    NEFEnsemble A = ef.make("A", 100, 1, "A", false);
    NEFEnsemble B = ef.make("B", 100, 1, "B", false);
    NEFEnsemble C = ef.make("C", 100, 1, "C", false);
    NEFEnsemble D = ef.make("D", 100, 1, "D", false);


    NEFEnsemble rule1a = ef.make("rule1a", 500, 2, "rule1a", false);
    NEFEnsemble rule1b = ef.make("rule1b", 500, 2, "rule1b", false);
    NEFEnsemble rule2 = ef.make("rule2", 200, 1, "rule2", false);
    rule2.collectSpikes(true);


    rule1a.addDecodedOrigin("OR", new Function[] { new MAX(2) },

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    NEFEnsemble B = ef.make("B", 100, 1, "B", false);
    NEFEnsemble C = ef.make("C", 100, 1, "C", false);
    NEFEnsemble D = ef.make("D", 100, 1, "D", false);


    NEFEnsemble rule1a = ef.make("rule1a", 500, 2, "rule1a", false);
    NEFEnsemble rule1b = ef.make("rule1b", 500, 2, "rule1b", false);
    NEFEnsemble rule2 = ef.make("rule2", 200, 1, "rule2", false);
    rule2.collectSpikes(true);


    rule1a.addDecodedOrigin("OR", new Function[] { new MAX(2) },
        Neuron.AXON);

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    NEFEnsemble C = ef.make("C", 100, 1, "C", false);
    NEFEnsemble D = ef.make("D", 100, 1, "D", false);


    NEFEnsemble rule1a = ef.make("rule1a", 500, 2, "rule1a", false);
    NEFEnsemble rule1b = ef.make("rule1b", 500, 2, "rule1b", false);
    NEFEnsemble rule2 = ef.make("rule2", 200, 1, "rule2", false);
    rule2.collectSpikes(true);


    rule1a.addDecodedOrigin("OR", new Function[] { new MAX(2) },
        Neuron.AXON);
    rule1b.addDecodedOrigin("AND", new Function[] { new MIN(2) },

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        Neuron.AXON);
    rule1a.doneOrigins();
    rule1b.doneOrigins();
    rule2.doneOrigins();


    NEFEnsemble output = ef.make("output", 500, 5, "fuzzyoutput", false);


    net.addNode(in);
    net.addNode(A);
    net.addNode(B);
    net.addNode(C);

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    FunctionInput input = new FunctionInput("input", new Function[] { f }, Units.UNK);


    // uiViewer.addNeoNode(uiInput);


    NEFEnsembleFactory ef = new NEFEnsembleFactoryImpl();
    NEFEnsemble integrator = ef.make("integrator", 500, 1, "integrator1", false);
    Termination interm = integrator.addDecodedTermination("input",
        new float[][] { new float[] { tau } }, tau, false);
    Termination fbterm = integrator.addDecodedTermination("feedback",
        new float[][] { new float[] { 1f } }, tau, false);

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        Units.UNK);


    // uiViewer.addNeoNode(uiInput);


    NEFEnsembleFactory ef = new NEFEnsembleFactoryImpl();
    NEFEnsemble integrator = ef.make("integrator", 500, 1, "integrator1",
        false);
    Termination interm = integrator.addDecodedTermination("input",
        new float[][] { new float[] { tau } }, tau, false);
    Termination fbterm = integrator.addDecodedTermination("feedback",
        new float[][] { new float[] { 1f } }, tau, false);

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    FunctionInput input = new FunctionInput("input", new Function[] { f }, Units.UNK);
    network.addNode(input);


    NEFEnsembleFactory ef = new NEFEnsembleFactoryImpl();


    NEFEnsemble integrator = ef.make("integrator", 500, 1, "integrator1", false);
    network.addNode(integrator);
    integrator.collectSpikes(true);


    // Plotter.plot(integrator);
    // Plotter.plot(integrator, NEFEnsemble.X);

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    PDF maxRatePDF = excitatoryProjection ? new IndicatorPDF(200f, 500f) : new IndicatorPDF(400f, 800f);
    ef.setNodeFactory(new LIFNeuronFactory(.02f, .0001f, maxRatePDF, interceptPDF));
    ef.setApproximatorFactory(new GradientDescentApproximator.Factory(
        new GradientDescentApproximator.CoefficientsSameSign(true), false));


    NEFEnsemble result = ef.make(name, num, 1);


    //TODO: bounding neurons for inhibitory projection
    //set intercepts of first few neurons to 1 (outside normal range)
    int n = 10;
    for (int i = 0; i < n; i++) {

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